{"uid":"cap_qlN7PLudxH0ltZcGMD6ks","slug":"agent-hub-text-embeddings-bge-m3-multilingual-4e6c9f5f","name":"Agent Hub Text Embeddings (BGE-M3 Multilingual)","description":"Text embeddings for semantic search and RAG (BGE-M3, multilingual, 1024 dimensions). Up to 100 texts per call, one flat price. Also callable as GET with the same fields as query parameters.","url":"https://agents.torbrooknews.com/v1/embed?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","properties":{"text":{"type":"string"},"texts":{"type":"string","description":"Up to 100 texts; separate items with |"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_e5JREna_2ZbHESXzuaU9U","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates multilingual text embeddings using BGE-M3 (1024-dimensional vectors) for semantic search and RAG pipelines, accepting up to 100 texts per call at a flat per-call price.","exampleAgentPrompt":"Can you generate BGE-M3 embeddings for these three sentences so I can index them in my vector database: 'The cat sat on the mat', 'Le chat est sur le tapis', 'El gato está en la alfombra'?","exampleUseCases":[{"title":"Multilingual RAG document indexing","prompt":"I have a batch of 50 product descriptions in English, French, and Spanish — can you embed all of them at once so I can store the vectors in my Pinecone index for semantic search?"},{"title":"Semantic similarity search pipeline","prompt":"I need to embed this user query 'best noise-cancelling headphones under $200' as a vector so I can find the most similar documents in my knowledge base."},{"title":"Cross-lingual FAQ retrieval","prompt":"Can you generate embeddings for these 20 FAQ questions in Japanese and Korean so I can match incoming user questions to the right answers using cosine similarity?"}],"resultDescription":"Returns 1024-dimensional dense float vectors (BGE-M3 embeddings) for each input text, suitable for semantic similarity, nearest-neighbor search, and retrieval-augmented generation workflows. One vector is returned per input text, preserving input order.","failureModes":["More than 100 texts submitted in a single call — exceeds batch limit","Empty or missing 'texts'/'text' query parameter — returns error","Payment not included or insufficient USDC — 402 Payment Required response","Extremely long individual texts may be truncated or rejected","Pipe character used within text content may cause incorrect splitting of batch"],"whenToPreferThis":"Choose this endpoint when you need multilingual, high-quality dense embeddings (BGE-M3, 1024-dim) for semantic search or RAG without managing your own embedding infrastructure. Ideal for batches of up to 100 texts at a flat $0.002 per call — extremely cost-efficient for bulk embedding. Prefer over OpenAI embeddings when multilingual coverage and open-weight reproducibility matter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T12:46:18.207Z","isFirstParty":false,"canonicalSlug":"agent-hub-text-embeddings-bge-m3-multilingual-4e6c9f5f"}